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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

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application of the Video Recognition Algorithm of Prosperous Tang Culture in the Multi-Frame Intelligent Generation Framework of the Zeitgeist Fragments of Chant Chang'an Poems 3

Application of the Video Recognition Algorithm of Prosperous...

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3rd international conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Duan, Yongsheng Xianyang Normal University College of Literature and Communication Xianyang712000 China

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

By analyzing the images of Chang'an used in the poetry of the prosperous Tang Dynasty, this paper briefly summarizes the specific meanings of these images of Chang'an in the poems, so as to draw the important influence and positive significance of the images of Chang'an in Li Bai's poetry creation. In the teaching of ideology and morality, some succinct, beautiful, recited and educational verses are used for teaching, which can not only stimulate students' interest in learning, but also enlighten their thinking. The similarity measurement method is used to smooth the surface level of graphics rendering, and the fusion method of object level and surface level follow-up information is used for animation image rendering, so as to realize the three-dimensional reconstruction and dynamic generation of multi-frame two-dimensional animation images. © 2022 IEEE.

关键词： Three dimensional computer graphics

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Towards Tumour Graph learning for Survival Prediction in Head & Neck Cancer Patients 1

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3rd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, held in Conjunction with the 25th international conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2022

作者： Juanco-Müller, Ángel Víctor Mota, João F. C. Goatman, Keith Hoogendoorn, Corné Canon Medical Research Europe Ltd. Edinburgh United Kingdom Heriot-Watt University Edinburgh United Kingdom

ISBN: (数字)9783031274206

ISBN: (纸本)9783031274190

With nearly one million new cases diagnosed worldwide in 2020, head & neck cancer is a deadly and common malignity. There are challenges to decision making and treatment of such cancer, due to lesions in multiple locations and outcome variability between patients. Therefore, automated segmentation and prognosis estimation approaches can help ensure each patient gets the most effective treatment. This paper presents a framework to perform these functions on arbitrary field of view (FoV) PET and CT registered scans, thus approaching tasks 1 and 2 of the HECKTOR 2022 challenge as team VokCow. The method consists of three stages: localization, segmentation and survival prediction. First, the scans with arbitrary FoV are cropped to the head and neck region and a u-shaped convolutional neural network (CNN) is trained to segment the region of interest. Then, using the obtained regions, another CNN is combined with a support vector machine classifier to obtain the semantic segmentation of the tumours, which results in an aggregated Dice score of 0.57 in task 1. Finally, survival prediction is approached with an ensemble of Weibull accelerated failure times model and deep learning methods. In addition to patient health record data, we explore whether processing graphs of image patches centred at the tumours via graph convolutions can improve the prognostic predictions. A concordance index of 0.64 was achieved in the test set, ranking 6th in the challenge leaderboard for this task. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Tumors

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An Architecture of Enhanced EMG Signal Classification based on Multi-Agent System 3

An Architecture of Enhanced EMG Signal Classification based ...

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3rd international conference on Intelligent Engineering and Management, ICIEM 2022

作者： Jain, Reema Garg, Vijay Kumar Lovely Professional University Department of Computer Application Punjab Phagwara India Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

An Electromyography (EMG) signal provides reliable information and represents an important step towards man-machine communication to address neuro-muscular movements. The vital aspects of this research focus on data acquisition, signal pre-processing, feature extraction, and accurate classification to meet the practical challenges in terms of reliability. The recorded signals first undergo pre-processing in which the uploaded EMG signals are first segmented into clusters using K-means followed by a Genetic Algorithm (GA) that precisely extracts the rows that exhibit potential signal data. The multi-agent system is designed using a Support Vector machine (SVM) with neural network architecture. This information of trained features deduced using SVM kernel function, also known as Support Vectors (SVs), is fed to Neural Network (NN) for the classification of EMG signals into two classes, namely, normal and pain. The performance of the proposed classification work is evaluated against the Electro- Myography-EMG dataset in terms of precision, sensitivity, f- measure, and accuracy. The simulation analysis over 1000 simulation rounds had demonstrated an average precision of 0.931, sensitivity of 0.915, f-measure of 0.923, and classification accuracy of 95.51%. The results illustrate a better classification accuracy when SVM support vectors are used by FFBPNN to learn the features of normal, and pain class in comparison to the existing EMG signal classification approaches. © 2022 IEEE.

关键词： Support vector machines

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3rd international conference on Electrical, Electronic Information and Communication Engineering, EEICE 2022

3rd International Conference on Electrical, Electronic Infor...

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2022 3rd international conference on Electrical, Electronic Information and Communication Engineering, EEICE 2022

The proceedings contain 126 papers. The topics discussed include: Internet of things detection and location based on remote communication fault of power consumption acquisition module;analysis and design of two-stage cascaded LED load wireless power transfer system;an adaptive threshold detection circuit for action potential;application of BP neural network in on-board charger fault diagnosis;comparative analysis of optimization schemes of carry look-ahead adder;single-phase disconnection fault location of active distribution network based on negative-sequence differential impedance;research on influence of battery energy storage system on voltage transient stability;multi-terminal detection and localization of partial discharge in transformer;an active power decoupling topology for three-phase four-leg inverters with unbalanced load;design and implementation of real-time vehicle speed monitoring device for front and rear vehicles;the online diagnosis of internal insulation fault of capacitance voltage transformers based on machine learning;and power distribution abnormal data identification and processing system based on correlation classification.

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Analysis of Stock Market Prediction Through the application of machine learning Techniques

Analysis of Stock Market Prediction Through the Application ...

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Disruptive Technologies (ICDT), international conference on

作者： Mukesh Kumar Bilal Ahmed Amit Jain Vikas Attri Rohini Mahajan Mirza Shuja Chandigarh Group of Colleges Mohali Punjab India Department of Computer Application Advanced Centre of Research & Innovation (ACRI) Chandigarh School of Business India School of Computer Applications Lovely Professional University Phagwara Punjab India Department of Computer Application Chandigarh School of Business India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Trends in stock market forecasting are acknowledged as a noteworthy and fruitful field of study. Smart investors can profit handsomely from the ups and downs of stock prices. Investors have a formidable obstacle when trying to forecast the stock market: data that is either stagnant or uncertain. For investors seeking to optimize profitability, stock market forecasting poses a significant difficulty. Mathematical methods and state-of-the-art learning technologies are employed to produce stock market projections. Computing methods, machine learning (ML) algorithms, performance variables, and reputable publications are just a few of the many subjects covered in this paper's review of different research articles. Therefore, the chosen articles are helping to advance datasets and ML algorithms that are used to predict how the stock market will move in the future. Predictions about the stock market are often made with the help of Neural Networks (NNs) and Artificial Neural Network (ANNs) algorithms. Unfortunately, there are still a lot of problems with the way the stock market is currently predicted. The principal premise of the research is that there are many interrelated factors that should be given greater weight when making stock market predictions.

关键词： Surveys Support vector machines machine learning algorithms Profitability Reviews machine learning Prediction algorithms Resource management Stock markets Forecasting

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Design of a Behavior Classification System for Monitoring Autism Spectrum Disorder using Deep learning 3

Design of a Behavior Classification System for Monitoring Au...

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3rd international Informatics and Software Engineering conference, IISEC 2022

作者： Villanueva, Alonica McCarthy, Sofia Louise Legaspi, Dj Paulo Mendoza, Denelle Jin Venal, Maria Cecilia Templonuevo, Sofia Coleen Rosales, Menchie Technological Institute of the Philippines Computer Engineering Department Quezon City Philippines

ISBN: (数字)9781665459952

ISBN: (纸本)9781665459952

A developmental disorder known as an autism spectrum disorder (ASD) is characterized by unusual social, communication, and behavioral situations. According to experts, children with ASD ranging from 2-16 years old need constant monitoring because of their unpredictable behavior. Meltdown and seizure are two of the most important aspects that the parents need to monitor because of their aftermath on children with ASD. This paper is aimed at designing a behavior classification system for monitoring children with ASD that classify the activity such as shaking to seizures and self-injurious behavior to meltdown, classifies the audio of the child, such as screaming and crying to meltdown, and designs a mobile application that has recommendations on the proper response of the parent or guardian. The system is built based on the SqueezeNext and Transformer algorithms, which utilize two Raspberry Pi 4B, equipped with a Web Camera responsible for the behavior classification and Re Speaker 2-Mics Pi HAT is responsible for the audio classification of the child with ASD. The audio and video classification system had 98.33% accuracy in classifying the child's behavior, and the device's alert notification and recommendation system had 100% system accuracy. The Behavior Classification System's design has proven to be effective in assisting parents and guardians of children with ASD. © 2022 IEEE.

关键词： Deep learning

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machine learning technology in safety production and maintenance of power plant 3

Machine learning technology in safety production and mainten...

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3rd international conference on computer Science and Communication Technology, ICCSCT 2022

作者： Wang, Shuqing Liu, Zhichao Zhang, Peng Zhang, Lei Chen, Shuqiao Qi, Nan Wu, Jiarun Eastern Inner Mongol Xieheke Left Back Banner Wind Power Generation Co. Ltd Inner Mongolia Tongliao China

ISBN: (纸本)9781510661240

Many safety accidents occur in electric power enterprises every year, more than 50% of which are caused by the lack of safety supervision, disregard for safety regulations and non-standard operation when operators are maintaining electric power equipment. In order to improve the professional quality of operators, we developed the power equipment maintenance training system based on virtual reality, which allows operators to operate in a 360-degree immersive virtual environment. In this system, we also developed an automatic online evaluation algorithm based on YOLOv3, and the experiment results shows that this method has good accuracy, intelligence and high reliability. © 2022 SPIE.

关键词： Virtual reality

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application of Deep learning in Imaging diagnosis of Brain diseases 3

Application of Deep Learning in Imaging diagnosis of Brain d...

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3rd international conference on machine learning, Big Data and Business Intelligence (MLBDBI)

作者： Liang, Junchen Wang, Zhaoze Ye, Xiangyang Xi An Jiao Tong Univ Sch Math & Stat Xian Peoples R China Wenzhou Kean Univ Dept Comp Sci Wenzhou Zhejiang Peoples R China Jilin Univ Coll Automot Engn Changchun Peoples R China

ISBN: (纸本)9781665417907

The brain is one of the most important parts of the human body, and the diagnosis of its diseases is of great significance to the treatment of diseases. With the rapid development of deep learning in recent years, its automatically extracted image features have significant advantages compared to traditional artificially extracted features. Therefore, more and more recognition methods based on deep learning are widely used in medical image recognition tasks (such as CT, MRI, PET-CT.). This paper will introduce the application of traditional methods and deep learning methods in various brain diseases. These methods are compared, analyzed, and summarized, then we explored their development status and future development trends.

关键词： brain disease medical image computer-assisted diagnosis deep learning

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Enhancing Fairness in Classification Tasks with Multiple Variables: A Data- and Model-Agnostic Approach 1

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3rd international Workshop on Algorithmic Bias in Search and Recommendation (BIAS) held as part of the 43rd European conference on Information Retrieval (ECIR)

作者： d'Aloisio, Giordano Stilo, Giovanni Di Marco, Antinisca D'Angelo, Andrea Univ Aquila Dept Engn & Informat Sci & Math Laquila Italy

ISBN: (数字)9783031093166

ISBN: (纸本)9783031093166;9783031093159

Nowadays assuring that search and recommendation systems are fair and do not apply discrimination among any kind of population has become of paramount importance. Those systems typically rely on machine learning algorithms that solve the classification task. Although the problem of fairness has been widely addressed in binary classification, unfortunately, the fairness of multi-class classification problem needs to be further investigated lacking well-established solutions. For the aforementioned reasons, in this paper, we present the Debiaser for Multiple Variables, a novel approach able to enhance fairness in both binary and multi-class classification problems. The proposed method is compared, under several conditions, with the well-established baseline. We evaluate our method on a heterogeneous data set and prove how it overcomes the established algorithms in the multi-classification setting, while maintaining good performances in binary classification. Finally, we present some limitations and future improvements.

关键词： machine learning Bias and fairness Multi-class classification Preprocessing algorithm

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Automatic 3D Teeth Semantic Segmentation with Mesh Augmentation Network 3

Automatic 3D Teeth Semantic Segmentation with Mesh Augmentat...

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3rd international conference on Pattern Recognition and machine learning, PRML 2022

作者： Zhao, Qijun Wu, Xiao Zhu, Feiyu Liu, Jun Wei, Mingying Peng, Jiahan Lu, Yin Sichuan University College of Computer Science Chengdu China Sichuan University National Key Laboratory of Fundamental Science on Synthetic Vision Chengdu China Sichuan University State Key Laboratory of Oral Diseases National Clinical Research Center for Oral Diseases Department of Orthodontics West China Hospital of Stomatology Chengdu China The People's Hospital of Neijiang Department of Stomatology Neijiang China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

In this paper, we propose an approach that improves segmentation networks with automatic augmentation networks for dental mesh data. Since conventional data augmentation is to augment all samples uniformly with predefined parameters, it ignores the unique characteristics of a single tooth sample and cannot make good use of the data set. And the traditional method separates data augmentation and segmentation network training, so the augmented data cannot be well adapted to the network to make it play a good role. We adopt a joint optimization strategy to integrate the augmentation network and the segmentation network, so that the augmented tooth data is the most suitable for the segmentation network. In addition, we design new improved loss functions suitable for augmentation and segmentation networks. Experiments have shown that the automatic augmentation network in our proposed method, named MeshAugNet, can effectively improve the segmentation accuracy after it is used for tooth segmentation. In general, this work achieves a combination of 3D dental data auto- augmentation network and segmentation network, which improves the accuracy of tooth segmentation, and can be used to solve the problem of too few samples in tooth datasets. © 2022 IEEE.

关键词： Semantic Segmentation

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